1. Field of Invention
The present invention relates to a method and system for foreground detection. More particularly, the present invention relates to a method and system for foreground detection using multi-modality fusion graph cut.
2. Description of Related Art
Currently available methods for performing the foreground region and background region segmentation on the video sequence obtained from a static camera include the following three:
The first method uses the frame difference. The absolute values for each of the pixels in the adjacent video frames of a video sequence (for example, the video frames at the 1st second and the 2nd second) are calculated. Assume that the greater the absolute value is, the more likely it is to be the foreground object. Thus, a threshold value may be set to determine the foreground objects. However, the shortcoming of this approach is that it is not appropriate for processing foreground objects that might stop at some point since there is no background model constructed beforehand. For example, if a person sitting in a chair is talking on the phone, the differences between adjacent images will be very small. It will be difficult to separate the foreground region from the background region, and thus this method will not apply.
The second method uses a single Gaussian distribution to construct the background model. This method requires a video sequence that is long enough. For the changes of each pixel during this time period, use single Gaussian probability distribution to construct a background model. Then, the new incoming frame is compared with the background model. If the similarity degree of the pixels compared is lower than a threshold value, these pixels will be considered as the foreground region. However, the shortcoming of this approach is that the changes in the video sequence need to have sufficient strength, so it may not be able to deal with non-static backgrounds. For example, when the camera is set up in the outdoors, the captured video sequences are often dynamic. The shadow of the building moves or changes over time. The outdoor water continues to have the water movement. Light changes to brighter or darker. These backgrounds change over time, and this method is not appropriate for them.
The third method uses an adaptive Gaussian Mixture Model to construct the background model. This method uses multiple sets of Gaussian distribution to model the background. Moreover, the background model may change with the incoming video frame for comparison. For example, one model is used during the day, and another model is used during the evening. Thus, the model is adaptive and able to overcome small changes in the background such as indoor to outdoor light. However, the shortcoming of this approach is that each pixel is regarded as independent, and the relationship between adjacent pixels are not taken into account. Therefore, this method is not appropriate when the foreground is similar to the background. For example, if the camera is set up in an surrounding that is not good for the surveillance camera, or the foreground object is very similar to the background, confusion might happen when this algorithm performs the segmentation. For instance, the color of the foreground clothing is so very similar to the background color that the foreground clothing is regarded as the background.
Therefore, there is a need to provide a method for foreground detection to improve the aforementioned shortcomings.